{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "MultiVariate_TimeSeries_AppliancesDataset.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyN1OgqgS+oj49pbrk7M3umv",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Divyanshu-ISM/Machine-Learning-Deep-Learning/blob/main/MultiVariate_TimeSeries_AppliancesDataset.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DlzWDhen2U52"
      },
      "source": [
        "import numpy as np\r\n",
        "import pandas as pd\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "\r\n",
        "import matplotlib as mpl\r\n",
        "mpl.rcParams['figure.figsize'] = (12, 4)\r\n",
        "mpl.rcParams['axes.grid'] = False\r\n",
        "# mpl.rcParams['style'] = 'default'\r\n",
        "\r\n",
        "from sklearn.preprocessing import MinMaxScaler, StandardScaler\r\n",
        "import tensorflow as tf"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZWmOWCn-3RA5"
      },
      "source": [
        "df = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/00374/energydata_complete.csv',index_col=0,parse_dates=True)"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 223
        },
        "id": "NA7GOeTU3i_N",
        "outputId": "ff4245b3-4ea1-4849-bb73-264e1bf83144"
      },
      "source": [
        "df.head(2)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Appliances</th>\n",
              "      <th>lights</th>\n",
              "      <th>T1</th>\n",
              "      <th>RH_1</th>\n",
              "      <th>T2</th>\n",
              "      <th>RH_2</th>\n",
              "      <th>T3</th>\n",
              "      <th>RH_3</th>\n",
              "      <th>T4</th>\n",
              "      <th>RH_4</th>\n",
              "      <th>T5</th>\n",
              "      <th>RH_5</th>\n",
              "      <th>T6</th>\n",
              "      <th>RH_6</th>\n",
              "      <th>T7</th>\n",
              "      <th>RH_7</th>\n",
              "      <th>T8</th>\n",
              "      <th>RH_8</th>\n",
              "      <th>T9</th>\n",
              "      <th>RH_9</th>\n",
              "      <th>T_out</th>\n",
              "      <th>Press_mm_hg</th>\n",
              "      <th>RH_out</th>\n",
              "      <th>Windspeed</th>\n",
              "      <th>Visibility</th>\n",
              "      <th>Tdewpoint</th>\n",
              "      <th>rv1</th>\n",
              "      <th>rv2</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2016-01-11 17:00:00</th>\n",
              "      <td>60</td>\n",
              "      <td>30</td>\n",
              "      <td>19.89</td>\n",
              "      <td>47.596667</td>\n",
              "      <td>19.2</td>\n",
              "      <td>44.7900</td>\n",
              "      <td>19.79</td>\n",
              "      <td>44.73</td>\n",
              "      <td>19.0</td>\n",
              "      <td>45.566667</td>\n",
              "      <td>17.166667</td>\n",
              "      <td>55.2</td>\n",
              "      <td>7.026667</td>\n",
              "      <td>84.256667</td>\n",
              "      <td>17.2</td>\n",
              "      <td>41.626667</td>\n",
              "      <td>18.2</td>\n",
              "      <td>48.900000</td>\n",
              "      <td>17.033333</td>\n",
              "      <td>45.53</td>\n",
              "      <td>6.600000</td>\n",
              "      <td>733.5</td>\n",
              "      <td>92.0</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>63.000000</td>\n",
              "      <td>5.3</td>\n",
              "      <td>13.275433</td>\n",
              "      <td>13.275433</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-01-11 17:10:00</th>\n",
              "      <td>60</td>\n",
              "      <td>30</td>\n",
              "      <td>19.89</td>\n",
              "      <td>46.693333</td>\n",
              "      <td>19.2</td>\n",
              "      <td>44.7225</td>\n",
              "      <td>19.79</td>\n",
              "      <td>44.79</td>\n",
              "      <td>19.0</td>\n",
              "      <td>45.992500</td>\n",
              "      <td>17.166667</td>\n",
              "      <td>55.2</td>\n",
              "      <td>6.833333</td>\n",
              "      <td>84.063333</td>\n",
              "      <td>17.2</td>\n",
              "      <td>41.560000</td>\n",
              "      <td>18.2</td>\n",
              "      <td>48.863333</td>\n",
              "      <td>17.066667</td>\n",
              "      <td>45.56</td>\n",
              "      <td>6.483333</td>\n",
              "      <td>733.6</td>\n",
              "      <td>92.0</td>\n",
              "      <td>6.666667</td>\n",
              "      <td>59.166667</td>\n",
              "      <td>5.2</td>\n",
              "      <td>18.606195</td>\n",
              "      <td>18.606195</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     Appliances  lights     T1  ...  Tdewpoint        rv1        rv2\n",
              "date                                            ...                                 \n",
              "2016-01-11 17:00:00          60      30  19.89  ...        5.3  13.275433  13.275433\n",
              "2016-01-11 17:10:00          60      30  19.89  ...        5.2  18.606195  18.606195\n",
              "\n",
              "[2 rows x 28 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sAWSHWtz4q6h"
      },
      "source": [
        "cols = ['Appliances', 'lights','T_out', 'RH_1', 'Visibility']\r\n",
        "\r\n",
        "df = df[cols]"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "id": "_5xeXs8F6Xq7",
        "outputId": "98e5319d-c819-4e39-ce84-0fdd683b2559"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Appliances</th>\n",
              "      <th>lights</th>\n",
              "      <th>T_out</th>\n",
              "      <th>RH_1</th>\n",
              "      <th>Visibility</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2016-01-11 17:00:00</th>\n",
              "      <td>60</td>\n",
              "      <td>30</td>\n",
              "      <td>6.600000</td>\n",
              "      <td>47.596667</td>\n",
              "      <td>63.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-01-11 17:10:00</th>\n",
              "      <td>60</td>\n",
              "      <td>30</td>\n",
              "      <td>6.483333</td>\n",
              "      <td>46.693333</td>\n",
              "      <td>59.166667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-01-11 17:20:00</th>\n",
              "      <td>50</td>\n",
              "      <td>30</td>\n",
              "      <td>6.366667</td>\n",
              "      <td>46.300000</td>\n",
              "      <td>55.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-01-11 17:30:00</th>\n",
              "      <td>50</td>\n",
              "      <td>40</td>\n",
              "      <td>6.250000</td>\n",
              "      <td>46.066667</td>\n",
              "      <td>51.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-01-11 17:40:00</th>\n",
              "      <td>60</td>\n",
              "      <td>40</td>\n",
              "      <td>6.133333</td>\n",
              "      <td>46.333333</td>\n",
              "      <td>47.666667</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     Appliances  lights     T_out       RH_1  Visibility\n",
              "date                                                                    \n",
              "2016-01-11 17:00:00          60      30  6.600000  47.596667   63.000000\n",
              "2016-01-11 17:10:00          60      30  6.483333  46.693333   59.166667\n",
              "2016-01-11 17:20:00          50      30  6.366667  46.300000   55.333333\n",
              "2016-01-11 17:30:00          50      40  6.250000  46.066667   51.500000\n",
              "2016-01-11 17:40:00          60      40  6.133333  46.333333   47.666667"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NeVn3ZJZ6Za9",
        "outputId": "52093533-8eda-43e5-a5c4-e07755b96d21"
      },
      "source": [
        "len(df)*0.80"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "15788.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qVR-ADNn7AIy"
      },
      "source": [
        "Data is collected every 10 mins."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GKL_Zz049Kvm"
      },
      "source": [
        "## Train-Test Split."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Epr_kLU6beT"
      },
      "source": [
        "df_train = df.iloc[:15800, :]\r\n",
        "df_test = df.iloc[15800: , :]"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vro4Nc7q_P-3",
        "outputId": "d5e00aa3-e007-45ea-a5b8-7be19910d04a"
      },
      "source": [
        "df_train.shape , df_test.shape"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((15800, 5), (3935, 5))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UNfpUKv8_qgr"
      },
      "source": [
        "## Data Scaling."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P5n0Rh69AIXv"
      },
      "source": [
        "scaler = MinMaxScaler()"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q3nyi1QWAINZ"
      },
      "source": [
        "df_train_scaled_values = scaler.fit_transform(df_train)\r\n",
        "\r\n",
        "df_train_scaled = pd.DataFrame(data = df_train_scaled_values , index = df_train.index , columns = df_train.columns)"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D6KhxyQPAIBC"
      },
      "source": [
        "df_test_scaled_values = scaler.transform(df_test)\r\n",
        "\r\n",
        "df_test_scaled = pd.DataFrame(data = df_test_scaled_values , index = df_test.index , columns = df_test.columns)"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IVCncfNqBEhF"
      },
      "source": [
        "# Creating Batches"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TbR5VclJBHKm"
      },
      "source": [
        "output_column = 'Appliances'"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "37CC2-HO_ovp"
      },
      "source": [
        "def batcher(look_up_time_steps,df,output_column):\r\n",
        "\r\n",
        "  l = look_up_time_steps\r\n",
        "\r\n",
        "  X = df.drop(output_column,axis=1)\r\n",
        "  y = df[output_column]\r\n",
        "\r\n",
        "  X_ = []\r\n",
        "  y_ = []\r\n",
        "\r\n",
        "  for i in range(l,len(df)):\r\n",
        "\r\n",
        "    batchX = X.iloc[i-l:i,:]\r\n",
        "    X_.append(batchX)\r\n",
        "\r\n",
        "    batchY = y.iloc[i]\r\n",
        "    y_.append(batchY)\r\n",
        "\r\n",
        "  X_ = np.array(X_)\r\n",
        "  y_ = np.array(y_)\r\n",
        "\r\n",
        "  return X_,y_"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f5EZ-6ODAG1s"
      },
      "source": [
        "X_train_scaled , y_train_scaled = batcher(520 , df_train_scaled , output_column)"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V7f7M4srG45J"
      },
      "source": [
        "X_test_scaled , y_test_scaled = batcher(520 , df_test_scaled , output_column)"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0Nf4s7QBHUb9"
      },
      "source": [
        "# Model Architecture"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zWpx8nXJHEYn",
        "outputId": "4a4ba8cb-8665-4ae9-b8ba-2bc77679d3c8"
      },
      "source": [
        "X_train_scaled.shape"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(15280, 520, 4)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K24v06U9Hedu"
      },
      "source": [
        "input_shape = (X_train_scaled.shape[1],X_train_scaled.shape[2])"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c8jdhBuUH4BK"
      },
      "source": [
        "from tensorflow.keras import Sequential\r\n",
        "from tensorflow.keras.layers import Dense,LSTM,Dropout , LeakyReLU"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T6hZJHiRH5dP"
      },
      "source": [
        "model = Sequential()\r\n",
        "\r\n",
        "model.add(LSTM(128, input_shape= input_shape, return_sequences=True))\r\n",
        "model.add(LeakyReLU(alpha=0.45))\r\n",
        "\r\n",
        "model.add(LSTM(128,  return_sequences=True))\r\n",
        "model.add(LeakyReLU(alpha=0.45))\r\n",
        "model.add(Dropout(0.4))\r\n",
        "\r\n",
        "model.add(LSTM(64, return_sequences=False))\r\n",
        "model.add(Dropout(0.2))\r\n",
        "\r\n",
        "model.add(Dense(1))\r\n",
        "\r\n"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JEOkIsS6KOpJ"
      },
      "source": [
        "Compile the model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uodl-yijJ1ZM"
      },
      "source": [
        "model.compile(optimizer='adam' , loss='mse')"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7OhU_MT2KhhH"
      },
      "source": [
        "Fit the model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2H1P-nonKgJk",
        "outputId": "16828084-8c06-4ecd-beb9-7648bbb5ede9"
      },
      "source": [
        "model.fit(X_train_scaled , y_train_scaled , batch_size=32 , epochs = 50 , shuffle=False)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/50\n",
            "478/478 [==============================] - 37s 58ms/step - loss: 0.0120\n",
            "Epoch 2/50\n",
            "478/478 [==============================] - 27s 57ms/step - loss: 0.0112\n",
            "Epoch 3/50\n",
            "478/478 [==============================] - 27s 57ms/step - loss: 0.0111\n",
            "Epoch 4/50\n",
            "478/478 [==============================] - 27s 57ms/step - loss: 0.0110\n",
            "Epoch 5/50\n",
            "478/478 [==============================] - 27s 57ms/step - loss: 0.0105\n",
            "Epoch 6/50\n",
            "478/478 [==============================] - 27s 57ms/step - loss: 0.0104\n",
            "Epoch 7/50\n",
            "478/478 [==============================] - 27s 57ms/step - loss: 0.0104\n",
            "Epoch 8/50\n",
            "478/478 [==============================] - 27s 57ms/step - loss: 0.0102\n",
            "Epoch 9/50\n",
            "478/478 [==============================] - 27s 57ms/step - loss: 0.0102\n",
            "Epoch 10/50\n",
            "478/478 [==============================] - 27s 57ms/step - loss: 0.0102\n",
            "Epoch 11/50\n",
            "478/478 [==============================] - 27s 57ms/step - loss: 0.0102\n",
            "Epoch 12/50\n",
            "478/478 [==============================] - 27s 57ms/step - loss: 0.0102\n",
            "Epoch 13/50\n",
            "478/478 [==============================] - 27s 57ms/step - loss: 0.0102\n",
            "Epoch 14/50\n",
            "478/478 [==============================] - 27s 57ms/step - loss: 0.0103\n",
            "Epoch 15/50\n",
            "478/478 [==============================] - 27s 57ms/step - loss: 0.0101\n",
            "Epoch 16/50\n",
            "478/478 [==============================] - 27s 57ms/step - loss: 0.0101\n",
            "Epoch 17/50\n",
            "478/478 [==============================] - 27s 57ms/step - loss: 0.0101\n",
            "Epoch 18/50\n",
            "478/478 [==============================] - 27s 57ms/step - loss: 0.0101\n",
            "Epoch 19/50\n",
            "478/478 [==============================] - 27s 57ms/step - loss: 0.0101\n",
            "Epoch 20/50\n",
            "478/478 [==============================] - 27s 57ms/step - loss: 0.0101\n",
            "Epoch 21/50\n",
            "478/478 [==============================] - 27s 57ms/step - loss: 0.0102\n",
            "Epoch 22/50\n",
            "478/478 [==============================] - 27s 57ms/step - loss: 0.0103\n",
            "Epoch 23/50\n",
            "478/478 [==============================] - 27s 57ms/step - loss: 0.0101\n",
            "Epoch 24/50\n",
            "478/478 [==============================] - 27s 57ms/step - loss: 0.0101\n",
            "Epoch 25/50\n",
            "478/478 [==============================] - 27s 57ms/step - loss: 0.0101\n",
            "Epoch 26/50\n",
            "478/478 [==============================] - 27s 58ms/step - loss: 0.0103\n",
            "Epoch 27/50\n",
            "478/478 [==============================] - 27s 57ms/step - loss: 0.0101\n",
            "Epoch 28/50\n",
            "478/478 [==============================] - 27s 58ms/step - loss: 0.0102\n",
            "Epoch 29/50\n",
            "478/478 [==============================] - 27s 57ms/step - loss: 0.0102\n",
            "Epoch 30/50\n",
            "478/478 [==============================] - 27s 57ms/step - loss: 0.0102\n",
            "Epoch 31/50\n",
            "478/478 [==============================] - 27s 57ms/step - loss: 0.0102\n",
            "Epoch 32/50\n",
            "478/478 [==============================] - 27s 57ms/step - loss: 0.0102\n",
            "Epoch 33/50\n",
            "478/478 [==============================] - 28s 58ms/step - loss: 0.0101\n",
            "Epoch 34/50\n",
            "478/478 [==============================] - 28s 58ms/step - loss: 0.0102\n",
            "Epoch 35/50\n",
            "478/478 [==============================] - 28s 58ms/step - loss: 0.0106\n",
            "Epoch 36/50\n",
            "478/478 [==============================] - 28s 58ms/step - loss: 0.0101\n",
            "Epoch 37/50\n",
            "478/478 [==============================] - 28s 58ms/step - loss: 0.0101\n",
            "Epoch 38/50\n",
            "478/478 [==============================] - 28s 58ms/step - loss: 0.0101\n",
            "Epoch 39/50\n",
            "478/478 [==============================] - 28s 58ms/step - loss: 0.0101\n",
            "Epoch 40/50\n",
            "478/478 [==============================] - 27s 57ms/step - loss: 0.0102\n",
            "Epoch 41/50\n",
            "478/478 [==============================] - 28s 58ms/step - loss: 0.0101\n",
            "Epoch 42/50\n",
            "478/478 [==============================] - 28s 58ms/step - loss: 0.0100\n",
            "Epoch 43/50\n",
            "478/478 [==============================] - 27s 57ms/step - loss: 0.0101\n",
            "Epoch 44/50\n",
            "478/478 [==============================] - 28s 58ms/step - loss: 0.0101\n",
            "Epoch 45/50\n",
            "478/478 [==============================] - 27s 57ms/step - loss: 0.0102\n",
            "Epoch 46/50\n",
            "478/478 [==============================] - 27s 57ms/step - loss: 0.0107\n",
            "Epoch 47/50\n",
            "478/478 [==============================] - 27s 57ms/step - loss: 0.0102\n",
            "Epoch 48/50\n",
            "478/478 [==============================] - 28s 58ms/step - loss: 0.0101\n",
            "Epoch 49/50\n",
            "478/478 [==============================] - 28s 58ms/step - loss: 0.0100\n",
            "Epoch 50/50\n",
            "478/478 [==============================] - 27s 57ms/step - loss: 0.0101\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7fc2d3ed7cf8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "GUebFxA0Kul9",
        "outputId": "53ad35f8-b843-4bcb-9907-f71d9ae10351"
      },
      "source": [
        "plt.style.use('default')\r\n",
        "losses = model.history.history['loss']\r\n",
        "plt.plot(range(len(losses)), losses)"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fc2c00feef0>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ulD6fnp6Ojz/+GL/97W+RlZUFpVKJG2+8Efn5+S5baL7GeV8XV3yIiIgGn0IIwZPzupjNZkRGRsJkMiEiIsIrX/PLE9VY/NY+ZCRF4NP/8x2vfE0iIiJ/4sn37yHR1eXPpBvaudVFREQ06Bh8ZNZd42PltRVERESDjMFHZrFdXV02u0Bjm2/eKUZEROQvGHxkFqRSIprXVhAREXkFg48P4LUVRERE3sHg4wN4lg8REZF3MPj4AJ7lQ0RE5B0MPj5A2uriig8REdGgYvDxAXHhjs6u2iZeVEpERDSYGHx8QDxrfIiIiLyCwccHxLHGh4iIyCsYfHwAV3yIiIi8g8HHBzi7uupaeG0FERHRYGLw8QExod3XVjS0ssCZiIhosDD4+ADXaysYfIiIiAYLg4+PcG53sc6HiIho8DD4+Aje10VERDT4GHx8BFd8iIiIBh+Dj4/gtRVERESDj8HHR3Cri4iIaPAx+PiI7q0udnURERENFgYfHxEX5jjLhys+REREg4fBx0ckRQUDAC7UtcDSaZN5NkRERP6JwcdHjEkIgyFCh1arDQVn6uSeDhERkV9i8PERCoUC2RMSAAD/LKmSeTZERET+icHHh8ydYAAAfH68ipeVEhERDQIGHx8yY2QswrVq1DRZcOhSo9zTISIi8jsMPj5Eo1Zi9rh4AMA/j3G7i4iIaKAx+PiYuRmO7a7tDD5EREQDjsHHx9w2Lh5BKgVOVzfjbE2z3NMhIiLyKww+PiZCF4QZI2MBcNWHiIhooDH4+KCcCXoADD5EREQDjcHHB2WPdwSfA2UNvMKCiIhoADH4+KCkqGBMGhYJIYAvTnDVh4iIaKAw+PgobncRERENPAYfHzU3wxF8dpXWotXaKfNsiIiI/AODj48apw9HckwwLJ12fH2qVu7pEBER+QUGHx+lUCiQM56HGRIREQ0kBh8f5tzu2nGiCp02u8yzISIiGvoYfHzYTanRiAoJQmNrB/ZfaJB7OkREREMeg48PU6uUmJOeAIDbXURERAOBwcfHze1qa//nMSOEEDLPhoiIaGhj8PFxt46Nh1atxMX6NpysapJ7OkREREMag4+PC9GoMWt0HABgewm3u4iIiK4Hg88QkCNtdzH4EBERXQ8GnyHgjvF6KBTAkXITKk1tck+HiIhoyGLwGQLiw7WYmhINAPicqz5ERETXjMFniOB2FxER0fVj8BkinMFnz9k6mNs7ZJ4NERHR0HRNwWf9+vUYMWIEdDodMjMzsXfv3j7Hb926Fenp6dDpdJg0aRK2bdvm8nkhBFavXo3ExEQEBwcjOzsbpaWlLmOKi4uRk5ODqKgoxMbGYunSpWhubr7ia7311luYPHkydDodEhISsHz58mt5iz5nVHwYRsWHosMmsPNkjdzTISIiGpI8Dj5btmzBihUrsGbNGhQXF2PKlCnIzc1FdXW12/EFBQVYsGABlixZgoMHD2L+/PmYP38+jh49Ko157rnn8OKLL+K1115DUVERQkNDkZubi/b2dgBARUUFsrOzMXr0aBQVFSE/Px8lJSV48MEHXb7WH/7wB/z617/GypUrUVJSgs8//xy5ubmevkWflTPBcWnpP0uMMs+EiIhoiBIemj59uli+fLn0e5vNJpKSkkReXp7b8ffee6+YN2+ey2OZmZli2bJlQggh7Ha7MBgMYu3atdLnGxsbhVarFZs3bxZCCLFhwwaRkJAgbDabNObw4cMCgCgtLRVCCFFfXy+Cg4PF559/7ulbkphMJgFAmEyma36NwbT/fL1IfeITkbE6X7RZO+WeDhERkU/w5Pu3Rys+VqsVBw4cQHZ2tvSYUqlEdnY2CgsL3T6nsLDQZTwA5ObmSuPPnTsHo9HoMiYyMhKZmZnSGIvFAo1GA6Wye7rBwcEAgN27dwMAtm/fDrvdjvLycowfPx7Dhw/Hvffei4sXL3ryFn3ajclRGBYVjGZLJ/73YLnc0yEiIhpyPAo+tbW1sNls0Ov1Lo/r9XoYje63X4xGY5/jnb/2NWbOnDkwGo1Yu3YtrFYrGhoasHLlSgBAZWUlAODs2bOw2+347//+bzz//PN47733UF9fj5ycHFitVrdzs1gsMJvNLh++TKlUYPEtIwAAb3x9FnY77+4iIiLyxJDo6srIyMDGjRuxbt06hISEwGAwIC0tDXq9XloFstvt6OjowIsvvojc3FzMmDEDmzdvRmlpKb788ku3r5uXl4fIyEjpIzk52Ztv65r8cHoKwnVqnK1twfbjbG0nIiLyhEfBJy4uDiqVClVVrt9wq6qqYDAY3D7HYDD0Od7569Ve8/7774fRaER5eTnq6urw9NNPo6amBiNHjgQAJCYmAgAmTJggPSc+Ph5xcXEoKytzO7dVq1bBZDJJH0NhWyxMq8bCGakAgNe/PivzbIiIiIYWj4KPRqPBtGnTsGPHDukxu92OHTt2ICsry+1zsrKyXMYDjnoc5/i0tDQYDAaXMWazGUVFRW5fU6/XIywsDFu2bIFOp0NOTg4A4JZbbgEAnDx5UhpbX1+P2tpapKamup2bVqtFRESEy8dQ8ODMEdColDhwoQEHLtTLPR0iIqKhw9PK6XfeeUdotVrx1ltviWPHjomlS5eKqKgoYTQahRBCLFy4UKxcuVIa/8033wi1Wi3+53/+Rxw/flysWbNGBAUFiSNHjkhjnn32WREVFSU+/PBDcfjwYXH33XeLtLQ00dbWJo156aWXxIEDB8TJkyfFyy+/LIKDg8ULL7zgMre7775bZGRkiG+++UYcOXJEfPe73xUTJkwQVqu1X+/N17u6enp86yGR+sQn4qcb98k9FSIiIll58v3b4+AjhCOEpKSkCI1GI6ZPny727NkjfW727NnigQcecBn/7rvvirFjxwqNRiMyMjLEp59+6vJ5u90ufvOb3wi9Xi+0Wq244447xMmTJ13GLFy4UMTExAiNRiMmT54s/vKXv1wxL5PJJP7jP/5DREVFiZiYGPG9731PlJWV9ft9DaXgU1plFqlPfCJGrPxEnK5ukns6REREsvHk+7dCCMHWoC5msxmRkZEwmUxDYtvrJxv34/PjVVgwPRl590yWezpERESy8OT795Do6iL3ls12FHa/X1yOmiaLzLMhIiLyfQw+Q9hNqdG4MSUK1k47Nhacl3s6REREPo/BZwhTKBRYdqtj1eevey6gxdIp84yIiIh8G4PPEJczwYARsSEwtXVgyz7fP4eIiIhITgw+Q5xKqcBPu1Z93tx9Dp02u8wzIiIi8l0MPn7g+1OHIzZUg/LGNnx6pFLu6RAREfksBh8/oAtS4YGZIwA4rrHgCQVERETuMfj4iYUzUhEcpEJJhRkFZ+rkng4REZFPYvDxE9GhGtx3s+N2+de+OiPzbIiIiHwTg48fWTIrDUoFsKu0FscqzHJPh4iIyOcw+PiR5JgQ/MukRADAG7vOyjwbIiIi38Pg42eW3ToKAPDxoQpUmtpkng0REZFvYfDxM5OGR2Ly8Eh02gX2nW+QezpEREQ+hcHHD42KDwMAlDdwxYeIiKgnBh8/NDw6GABQ3tgq80yIiIh8C4OPH3IGn0tc8SEiInLB4OOHhkeHAGDwISIiuhyDjx/qXvFp5fUVREREPTD4+KHEyGAoFEB7hx11LVa5p0NEROQzGHz8kEathD5cB4DbXURERD0x+PgpqbOLwYeIiEjC4OOnetb5EBERkQODj59iZxcREdGVGHz8FFd8iIiIrsTg46eG8RBDIiKiKzD4+KmeW108y4eIiMiBwcdPJUU52tnbOmxoaO2QeTZERES+gcHHT2nVKugjtABY50NEROTE4OPH2NlFRETkisHHj7Gzi4iIyBWDjx8bFsXOLiIiop4YfPwYt7qIiIhcMfj4MW51ERERuWLw8WM9LyrlWT5EREQMPn4tqavGp8VqQyPP8iEiImLw8We6IBUSwp1n+bDOh4iIiMHHzw1jnQ8REZGEwcfPsbOLiIioG4OPn2NnFxERUTcGHz8ndXY1csWHiIiIwcfPcauLiIioG4OPn+ve6uJZPkRERAw+fs55X1ezpROmNp7lQ0REgY3Bx8/pglSIC+NZPkRERACDT0BgZxcREZEDg08A6FnnQ0REFMgYfAIAO7uIiIgcGHwCAFd8iIiIHBh8AgDv6yIiInJg8AkAyc7Tm3mWDxERBbhrCj7r16/HiBEjoNPpkJmZib179/Y5fuvWrUhPT4dOp8OkSZOwbds2l88LIbB69WokJiYiODgY2dnZKC0tdRlTXFyMnJwcREVFITY2FkuXLkVzc7Pbr1dXV4fhw4dDoVCgsbHxWt6iXxkW5ajxabJ0wtzWKfNsiIiI5ONx8NmyZQtWrFiBNWvWoLi4GFOmTEFubi6qq6vdji8oKMCCBQuwZMkSHDx4EPPnz8f8+fNx9OhRacxzzz2HF198Ea+99hqKiooQGhqK3NxctLe3AwAqKiqQnZ2N0aNHo6ioCPn5+SgpKcGDDz7o9msuWbIEkydP9vSt+a1gjQpxYRoAwKVGbncREVEAEx6aPn26WL58ufR7m80mkpKSRF5entvx9957r5g3b57LY5mZmWLZsmVCCCHsdrswGAxi7dq10ucbGxuFVqsVmzdvFkIIsWHDBpGQkCBsNps05vDhwwKAKC0tdXntV155RcyePVvs2LFDABANDQ39fm8mk0kAECaTqd/PGSr+7eXdIvWJT0T+0Uq5p0JERDSgPPn+7dGKj9VqxYEDB5CdnS09plQqkZ2djcLCQrfPKSwsdBkPALm5udL4c+fOwWg0uoyJjIxEZmamNMZisUCj0UCp7J5ucLCjbmX37t3SY8eOHcPvfvc7/OUvf3EZ2xuLxQKz2ezy4a/Y2UVEROThVldtbS1sNhv0er3L43q9Hkaj0e1zjEZjn+Odv/Y1Zs6cOTAajVi7di2sVisaGhqwcuVKAEBlZSUAR4hZsGAB1q5di5SUlH69n7y8PERGRkofycnJ/XreUDQ8ip1dREREQ6KrKyMjAxs3bsS6desQEhICg8GAtLQ06PV6aWVn1apVGD9+PH784x/3+3VXrVoFk8kkfVy8eHGw3oLsuOJDRETkYfCJi4uDSqVCVVWVy+NVVVUwGAxun2MwGPoc7/z1aq95//33w2g0ory8HHV1dXj66adRU1ODkSNHAgC++OILbN26FWq1Gmq1GnfccYc05zVr1ridm1arRUREhMuHv+LpzURERB4GH41Gg2nTpmHHjh3SY3a7HTt27EBWVpbb52RlZbmMB4Dt27dL49PS0mAwGFzGmM1mFBUVuX1NvV6PsLAwbNmyBTqdDjk5OQCA999/H4cOHcK3336Lb7/9Fn/84x8BALt27cLy5cs9eZt+abh0lg+3uoiIKHCpPX3CihUr8MADD+Cmm27C9OnT8fzzz6OlpQWLFy8GACxatAjDhg1DXl4eAODRRx/F7NmzsW7dOsybNw/vvPMO9u/fj9dffx0AoFAo8Nhjj+GZZ57BmDFjkJaWht/85jdISkrC/Pnzpa/78ssvY+bMmQgLC8P27dvxq1/9Cs8++yyioqIAAKNGjXKZZ21tLQBg/Pjx0phA5jy92dzeCVNbByKDg2SeERERkfd5HHzuu+8+1NTUYPXq1TAajbjhhhuQn58vFSeXlZW5dFTNnDkTmzZtwlNPPYUnn3wSY8aMwQcffICJEydKYx5//HG0tLRg6dKlaGxsxKxZs5Cfnw+dTieN2bt3L9asWYPm5makp6djw4YNWLhw4fW894ASolEjNlSDuhYryhvaGHyIiCggKYTgHQZOZrMZkZGRMJlMflnv828v78bhSya8vnAa5ma4r8kiIiIaajz5/j0kurpoYLCzi4iIAh2DTwBhZxcREQU6Bp8AInV28b4uIiIKUAw+AYRbXUREFOgYfAIIt7qIiCjQMfgEkGFd93WZ2jpgbu+QeTZERETex+ATQEK1akSHOM7vKeeqDxERBSAGnwDD7S4iIgpkDD4Bhnd2ERFRIGPwCTDs7CIiokDG4BNguNVFRESBjMEnwDg7uy7xEEMiIgpADD4BZngMt7qIiChwMfgEGOeKT2NrB5p4lg8REQUYBp8AE64LQpTzLJ9GrvoQEVFgYfAJQN0t7Qw+REQUWBh8AtDwKFm4mVkAACAASURBVHZ2ERFRYGLwCUDDpLN82NlFRESBhcEnAPEQQyIiClQMPgGIhxgSEVGgYvAJQMO51UVERAGKwScAOWt8Glo70GLplHk2RERE3sPgE4AidEGIDOZZPkREFHgYfAKUdGcXt7uIiCiAMPgEKGedz8V6rvgQEVHgYPAJUOMM4QCAnSerZZ4JERF5os1qw8eHKmDmfYvXhMEnQN0zdTgAYOepGtb5EBENIX8ruoCfbz6Il784LfdUhiQGnwCVFheKmaNiIQSwZd9FuadDRET9dK62BQBQfKFB5pkMTQw+AWzB9BQAwJZ9Zei02WWeDRER9UdNkwUAcLzSDLtdyDyboYfBJ4DNzdAjJlSDKrMFX56skXs6RETUD7XNjuDTYrXhQj07cz3F4BPAtGoVfjDNUeuzeW+ZzLMhIqL+qOkKPgBwrMIs40yGJgafAHffzckAHN1dLHImIvJtQghpqwsAjlWaZJzN0MTgE+BGxocha2Qs7AJ4l0XOREQ+rcVqQ3tHd01mCVd8PMbgQ1iQ6Shyfnf/RRY5ExH5sJ6rPQC3uq4Fgw8ht6vIudLUjp0sciYi8lnO4JMQroVCAVQ3Wa4IQ9Q3Bh+CVq3Cv7PImYjI5zk7ulJiQpAWFwoAOFbJVR9PMPgQAOCHXUXOX56sRgWLnImIfJJzdSc+XIsJiREAuN3lKQYfAuAocp4xMsZR5LyfRc5ERL7IueITH65FRlIkAK74eIrBhyTdJzlfhI2ngRIR+Rznik9cmBYTkhwrPiUVbGn3BIMPSXIzDIgOCUKlqR1fneKt7UREvsbdVte52ha0WjvlnNaQwuBDEl2QCt/vurV9UxG3u4iIfI201RWmRXy4FgnhWggBnDA2yTyzoYPBh1w4z/T54kQVKk0sciYi8iXSVle4FgB6bHexzqe/GHzIxaj4MGSmdRU577sk93SIiKiLEAK1zVYAjq0uAOzsugYMPnSF+zOdRc5lLHImIvIR5rZOWLtO148L0wDoXvFhZ1f/MfjQFXIzDIgKCUKFqR1fn+JJzkREvqCmuR0AEKFTQ6tWAYDU0n6i0swrh/qJwYeu4FLkzJOciYh8QnWPji6n1JgQhGhUsHTaca62Ra6pDSkMPuSW80yfL05Uw2hql3k2RER0eX0PACiVCoxP5HaXJxh8yK3RCWGYnhYDm13gr3vOyz0dIqKA1/Pwwp4y2NnlEQYf6tWirFQAwCs7z+CzI5Uyz4aIKLDVuNnqAtjZ5alrCj7r16/HiBEjoNPpkJmZib179/Y5fuvWrUhPT4dOp8OkSZOwbds2l88LIbB69WokJiYiODgY2dnZKC0tdRlTXFyMnJwcREVFITY2FkuXLkVzc7P0+UOHDmHBggVITk5GcHAwxo8fjxdeeOFa3h51mTcpEQump0AI4NF3vsWes3VyT4mIKGA5Dy+8fMWnZ2eXEOzEvRqPg8+WLVuwYsUKrFmzBsXFxZgyZQpyc3NRXe3+ioOCggIsWLAAS5YswcGDBzF//nzMnz8fR48elcY899xzePHFF/Haa6+hqKgIoaGhyM3NRXu7o7akoqIC2dnZGD16NIqKipCfn4+SkhI8+OCD0mscOHAACQkJePvtt1FSUoJf//rXWLVqFV5++WVP3yJ1USgUeGb+RMydoIfVZsdPN+7Hce4hExHJorcVn7H6cKiUCtS3WGE0sybzqoSHpk+fLpYvXy793maziaSkJJGXl+d2/L333ivmzZvn8lhmZqZYtmyZEEIIu90uDAaDWLt2rfT5xsZGodVqxebNm4UQQmzYsEEkJCQIm80mjTl8+LAAIEpLS3ud68MPPyxuv/32fr83k8kkAAiTydTv5wSCNmun+MGrBSL1iU/Ezc9sF2V1LXJPiYgo4Nz1/Nci9YlPxBcnqq743Nw/fCVSn/hEfH7MKMPM5OfJ92+PVnysVisOHDiA7Oxs6TGlUons7GwUFha6fU5hYaHLeADIzc2Vxp87dw5Go9FlTGRkJDIzM6UxFosFGo0GSmX3dIODgwEAu3fv7nW+JpMJMTExvX7eYrHAbDa7fNCVdEEqvLHoJozTh6O6yYIH/rQX9S1WuadFRBRQet7TdTlpu4t1PlflUfCpra2FzWaDXq93eVyv18NoNLp9jtFo7HO889e+xsyZMwdGoxFr166F1WpFQ0MDVq5cCQCorHRfdFtQUIAtW7Zg6dKlvb6fvLw8REZGSh/Jycm9jg10kSFBeOs/bkZSpA5na1uw+K19vA2YKACY2ztQXNbA2hGZ2ewCdS1XtrM7sbOr/4ZEV1dGRgY2btyIdevWISQkBAaDAWlpadDr9S6rQE5Hjx7F3XffjTVr1mDu3Lm9vu6qVatgMpmkj4sXeSN5XxIjg/GXJdMRFRKEQxcb8fDfitHBk0KJ/NpT/3sU97xSgGc+Pc7wI6OGVitsdgGFAogJ1Vzx+Qk8y6ffPAo+cXFxUKlUqKqqcnm8qqoKBoPB7XMMBkOf452/Xu0177//fhiNRpSXl6Ourg5PP/00ampqMHLkSJfnHTt2DHfccQeWLl2Kp556qs/3o9VqERER4fJBfRudEI4/PXgzdEFK7DxZg5XvH+E/hkR+7MCFBgDAm7vP4Q/bT8k8m8Dl3OaKDtEgSHXlt27nVldZfSvM7R1endtQ41Hw0Wg0mDZtGnbs2CE9ZrfbsWPHDmRlZbl9TlZWlst4ANi+fbs0Pi0tDQaDwWWM2WxGUVGR29fU6/UICwvDli1boNPpkJOTI32upKQEt99+Ox544AH813/9lydvjTwwNSUar/xoKlRKBd4vvoTf55+Ue0pENAia2jtQ3tgm/f6lL07jlZ2nZZxR4JI6utzU9wBAVIgGw6Icta/Hud3VJ4+3ulasWIE33ngDGzduxPHjx/HQQw+hpaUFixcvBgAsWrQIq1atksY/+uijyM/Px7p163DixAk8/fTT2L9/Px555BEAjpbpxx57DM888ww++ugjHDlyBIsWLUJSUhLmz58vvc7LL7+M4uJinDp1CuvXr8cjjzyCvLw8REVFAXBsb91+++2YO3cuVqxYAaPRCKPRiJoaXrI5GOak65F3zyQAwGtfncFb35yTeUZENNBOVTnOSjNE6LDyrnQAwHP5J/Fn/n33Oqmw2U19jxOvrugftadPuO+++1BTU4PVq1fDaDTihhtuQH5+vlScXFZW5lJ3M3PmTGzatAlPPfUUnnzySYwZMwYffPABJk6cKI15/PHH0dLSgqVLl6KxsRGzZs1Cfn4+dDqdNGbv3r1Ys2YNmpubkZ6ejg0bNmDhwoXS59977z3U1NTg7bffxttvvy09npqaivPnz3v6Nqkf7r0pGTVNFqz9x0nkfXYC8yYn9fmXkoiGllNVTQCAsYZw/Gz2KLRaOvHiF6fx24+PIUSjwn03p8g8w8DRfV3FlfU9ThOSIvD58Sp2dl2FQrBAQ2I2mxEZGQmTycR6n34SQuCeVwtwsKwRy2aPxKq7xss9JSIaIL/9uAR//uY8fjIrDU99dwKEEPivT4/jj7vPQaEAnr/vBtx9wzC5pxkQ/uvTY3hj1zn89Dtp+PW8CW7H/KPEiGV/PYAJiRHY9uh3vDxDeXny/XtIdHWR71IoFPj5nNEAgL8WXkADz/ch8hs9V3wAx9/3X88bjx9lOq6yWfHuIfyjxP1RJjSw3N3MfjlnZ1dpdROsney47Q2DD12328clYEJiBFqtNu79E/mRk0ZHjc84fbj0mEKhwH/ePRH3TB0Gm13g55sO4qtTrKUcbL3dzN7T8OhgROjU6LAJnK5u7nVcoGPwoevWc9XnzwXn2UpJ5Afqmi1SQe0YfZjL55RKBZ77/mT8yyQDrDY7lv5lPy8xHmS93dPVk0KhkNraSypMXpnXUMTgQwMiN8OA0QlhaGrvxF8LL8g9HaJ+OXChHqZWBnV3nB1dKTEhCNFc2QejVinx/H03Yk56Aiyddix5ax+/2Q6i3m5mv9yExEgA7OzqC4MPDQilUoFHbnes+vxx11leZ0E+7+tTNfj+q4X41XuH5J6KT5Lqe3psc11Oo1bilR9NRdbIWLRYbdjw1VlvTS+gdNjsqG+9eo0PwDu7+oPBhwbMdycnIjU2BA2tHdhUVCb3dMiHtHfYsPDNIvzu42NyT0XyzelaAMDOkzUM6m6c7Ao+4wxhfY7TBanwi5yxAIBdpTWw2dkoPNDqW6wQAlApFYgO6b2dHei+s+tYpZmn6veCwYcGjFqlxMO3jQIAbPj6LNo7bDLPiHzF3nP12FVai7cKzvlMyPj2YiMAwGqzo/AM61Mud8p49RUfp6kpUQjXqdHQ2oHDlxoHe2oBx1nfExOqgUqp6HPsqPgwaFRKNLV34lJDW59jAxWDDw2o7904HEmROtQ0WbB1Py99JYfiMsd9T3YBHK9sknk2jpuuj5Z316PsPMmupJ6EED1WfK4efNQqJWaNjgMAdngNgprmvq+r6EmjVkrF6Lyp3T0GHxpQGrUSP+ta9Xntq7M8S4IAdF90CQDHfKAA9kxNM1qs3SuSO09Vc1ugB6O5HU3tnVApFUiLC+3Xc24bFw+AIXIw9Kejqydpu8sH/q75IgYfGnD33pSM+HAtyhvb8MHBcrmnQzKz24W0rQT4xk+hzvlMGhYJjUqJi/VtOFvbIvOsfIezoystLhRatapfz5k9NgEAcOhSI+p5kOmA6m9Hl9ME3tnVJwYfGnC6IBWW3ToSAPDKztPotHHVJ5CdrmlGU3t3XY8vBB9nHUrWqFjcnBYNAPiKKxUSZ33PuH7U9zgZInVIN4RDCEeRMw0cT1d8JiR1tbT7wN81X8TgQ4Pi/swURIcE4XxdKz49Uin3dEhGzm2u4dHBAICTxiZ0yByGD110bAFMGR6F27pWKnayNkVysh+t7O7M7truYogcWJ4Gn/GJjv/fKkztvEbIDQYfGhQhGjV+8h3Hqs/LX5yGnS2uQ1J7hw1fnqi+rqBS3BV8/nVKEsJ1alhtdpRWyXecfnuHDce7tgCmJEdKtSl7ztahzcpORKD7DJ+rtbJfzhkivy6t4d/5AdS91dV3K7tTuC4IqbEhALjd5Q6DDw2ahVmpCNepUVrdjH8e40WGQ9FrX53B4rf24bWdZ675NZwdXTelRku1B3Ke8Hu80oxOu0BsqAbDooIxOiEMw6KCYe2089oFOGqy+nN4oTvTUqMRqlGhttnqE1ua/sLTFR+gR50P/3+4AoMPDZoIXRAWzxwBAHjpi9PsmhmCnEHgn8eqrun5ja1WnKlxFA3fmBKNjK7aAzm/KR7qKmyekhwFhUIBhUIhbdHsPFkt27x8xcWGVrR32KFRK5Ea27+OLieNWolbutra+Wc5cJzBJ+Eagg+vEbkSgw8NqsW3pCFEo0JJhdlv21wvNbTik8MVfhfs7HaBknJHQDlaYUJd13K7Jw6WOUJGWlwoYkI1mDhM/n+MD19yfO3JwyOlx2aP7apNYZ0PTnYVNo9JCLvqYXnuSHU+/LMcEJZOG8xdzQH97eoCgInDHP999+yoJAcGHxpU0aEaLJyRCgB4fkep7EWtA00IgZ9s3I9HNh3EjuP+9RNuWX0rmiyOf3CFAHZ3XfHgCec219QUR+dURo9uE7lqQL691L3i43TL6DgEqRQ4X9eK8wHe1i7V93i4zeV02zhHnU9xWQMvgB0Atc2O4uQglQKRwUH9ft5NI6KhUjr+my5v5AnOPTH40KBb8p006IKUOHSxET/6Y5FUqOcPCs/U4UTXT8j+9hPukXLXVZldpZ4HH2dH19RUR8gYFR8KrVqJFqsNF+pbr3+SHjK1deBs19bblOHdwSdMq8ZNqTEAuEVzsqvwfGw/Tmx2Z1hUMMYkhMEugF2n/evvhByk+p4wLRSK/q/AheuCMKlr1afgGn5o8WcMPjToEsJ1eOVHUxGmVWPvuXr820u7ceSSf+w7byw8L/1vfyuMdV7pMDLeUeexq7TGo+08m11I9TTOFR+1Son0rtqDo+Xe/2/A+TWTY4IRE+raISOdPOxnAdZTpVJhs2cdXT1JW4d+ur3tTbVdwSfOg/oep5mjYgGAd9FdhsGHvGJOuh4fLL8FI+NCUWFqx7+/VoC/F1+Se1rX5VJDK7b3KPotrW6WfjrzB0e76nAenDkCWrUSVWYLSqv734Z+0tiEFqsNYVq1S3eQ8zh9OQqcnfUOPVd7nJxbNIVn6gL2gt0Omx1narpWfK5xqwvo/rP86pRnYZmu5Mk9XZdzFpp/c6aW/z/0wOBDXjM6IQwfPHIL5qQnwNJpx4p3D+E/Pzk2ZE92fntPGewCuGV0LMZ3rWL4y6qPEAJHuwqbp6ZEI3Ok4yfHrz1YDTnQVd9zQ3KUS5Fsd/Dx/oqP88Rmd8FnrD4MhggdLJ12FJ2r9/bUfML52hZ02ARCNSoMiwq+5te5OS0awUEqVDdZfOJS2qFMWvG5huAzLTUamq4fWnglSzcGH/KqCF0Q/rjoJvx8zmgAwJu7z2HRn/YOubt92jts2LKvDADwQNYIzBjpqA8p9JPgc7G+Daa2DmhUSozVh+PWMY6fHL/2oM7noFTfE+3y+MQeLe3e/ilUOrE5+crgo1Aoely0GZh1PtKJzYZwj+pJLqdVq6Rtlp2nAvPPcqBIKz7XsNWlC1JhWtc2M+t8ujH4kNcplQr8cu44vPbjqQjRqFBwpg7/+tLuIXXexEeHKtDQ2oFhUcG4Y7weWV0rIv6y4uPc5kpPDIdGrcStXTUbRWf7vw3U3dHlGjLGGcKhUipQ32KF0dw+gLPuW5W5HUZzO5QKSG31l7stwK9cuJY7unrD29oHxrUcXtiTM4AWsM5HwuBDsrlzYiL+9+FbkBobgvLGNnz/1QJ8+G25z+9FCyGwseA8AMfp1CqlAplpsVAogLM1Lajy4jfzweLs6HK2n49JCIM+QgtLpx37zl99G6i22YLzdY6urRuTXVd8dEEqjI53FM46zwnyBmeh9Vh9OEI0ardjbhkdB7VSgbO1LSir837Xmdyu9Y4ud5y3tRdfaIC5nW3t18rTm9kvN7OrzqfwbB2vEenC4EOyGmcIx0fLZ2H22Hi0d9jx6Dvf4q4XduHtPRfQbOm8+gvIoLisASUVZmjVStx3UzIAIDIkSKpd8YdVH2f3k7MdVqFQ4DtjHD/B96et3Xlw4ZiEMESGXHn2iBwFzof6qO9xCtcFYVrX1lwgbtGc6mplH3eNrew9pcSGYGRcKDrtgtss1+F6V3wmD49EqEaFxtYO3tvVhcGHZBcZEoQ/PXgzfj5nNLRqJU4Ym/DUB0cx47934KkPjuCE0bf+sr5VcAEAMP+GYYju0RI9I80/Wkcdhc2O4NNzS8i53dWfAufLDy68XEZXoDrqxe3Nvup7epodoFs07R02nK9zFMAOxIoPELh/lgPpeoNPkEopNScUnGEABRh8yEeouup+ip68A0/NG4+RcaFotnTi7T1luPP5Xfj3VwvwwcFyWDrlbTOuMrfjsyOVAIBFM1NdPpc1yj/qfMob29DQ2oEglcLlJ/9Zo+OgUAAnjE2ovsp2XvFlBxdezrni460LFO12IXV09byqwh3nDeOB1tZ+uroZQgAxoZp+3wJ+NT2vAvH1LWxf1GrtRIvV8d/g9fx/wjofVww+5FOiQjT4yXdGYscvZ+NvP8nEXRMNUCkV2H+hAY9t+RZZeV8gb9txfH2qRpbj8P9WVIZOu8DNI7ov3HS6OS0GSgVwvq4Vlaahe0S8s419rD4cWrVKejwmVCN1ZPW13dVhs0vbStNS3a/4TOgKPuWNbWjwQkff+boWmNs7oVUrr7qNMz4xHPoILdo6bP2qZ/IXPe/oup6Orp5mjIyFVq1Epald2kaj/qttcvzd0AUpEaZ1X5fWHzNHOep89p6rh7VzaB4fMpAYfMgnKRQK3DI6Dq/+eBoKVs7BL7LHwhChQ32LFRu+PotFf9qLKb/7J25b+yUefecg3tx9Dgcu1A/qT+jWTjs2FXW1sHfdOt9TRI8j4ofydpe0zZV05crId7ra2neV9r51caKyCe0ddkTo1BgZ5/703whdEFJjQwB4p87HeTFpRlIEglR9/7OnUCiklYpA2qI5Vd3V0TUA9T1OuiCVtBL6VQDWTF2vmmbHymp8uGfXVVwu3RCOmFANWq02aeUzkDH4kM/TR+jwaPYY7H7idmxYOA3/OiUJKTGOb5rn61rx4bcV+M9PjuH7rxYiY80/8C8v7MKqvx9B0QBvOX12tBK1zRboI7TIzTC4HTNj5NCv83F2dE10syXkrPPZfbq21w4RZ33PjSnRUPZxu7c3DzKUTmy+Sn2Pk/Pk4UA6z8fZyj5Q9T1OgRgiB0pN14rPtXZ0OSmVCunIjW9OD91/mwYKgw8NGWqVErkZBry04EZ8/fjtOPibHLy1+GasyBmL7PEJiAvTwmYXOFZpxua9Zbjv9T348R+LpG/E1+utrhb2H2Wm9rpqMMNZ53NuaP7j4lLYnHTlWTdTU6IRolGhttmK470UnTsvJu1tm8spo8dBhoPNufV2Qz+Dzy2j46BSKnCmpgUXr3KZqhACX5yowrv7Lw7pbYSB7OjqyRki952v99lOTV91PddVXC5rFAucna5905BIZtGhGtw2LkH6h1UIgUpTOw5fasTOkzV4v/gSdp+uxe7TtZiTnoAVOWMxcVjfha29OXypEQfLGhGkUmDB9JRex908IgYqpQIX69twqaEVw6NDrunrycVobkddixUqpUK6hqMnjVqJrJGx2HGiGl+fqr2izgm4ekeXk7dWfDpsdilcTe6jlb2nyOAgTE2Jwr7zDfjqVA1+PCPV7bgzNc14+qMSqebp1Z1nsOqudORM0A9YnYw3NLV3oLzRUZc2NmFgg8+I2BCkxISgrL4VhWfqkDNBP6Cv78+ut6OrJ+e9XQfLGtFmtSFYo7rKM/wXV3zIbygUCiRFBePOiYl49vuT8cUvb8MPpg2HSqnAFyeq8d2XduOhtw/gVJXndwdt7Gphnzcpsc9/hMK0aqlraChudzkLm8ckhEEX5P4fRud2l7s6n2pzOy41tEGhAKYk9x0ynaHpbG0LWgZxJeCksQnWTkfN0YjY/gfR7u2uK99ni6UTz352Anc+/zV2ldZCo1IiJlSDc7UtWPrXA7j/jSLZTyLvsNlh6+eBdc7VHkOEzu25S9eDV4Fcu+s9vLCnEbEhSIzUwWqzY/+FwCnad4fBh/xWckwI1v5gCrb/4lbcfUMSFArgs6NG5D7/NR595yDO1vSvy6Su2YKPD1cAcF/UfDmpzmcItrVL9T19rIw5C5z3n29Aq9U1sDhXe8bpwxGu6/sbaHy4FgnhWgiBQT2rSTq4MDnKo1UYZ21KwZla6RgFIQQ+PVyJ7D98hde+OoMOm8Dt4+Lxz1/ciq9+dRsevm0UNGolCs/W4bsv7cbj7x26auv/YLhY34pZv/8C97zyTb+OgDjV446uwdDz+gq2tfffQK74KBQKqbsr0NvaGXzI742MD8MLP7wR/3jsVtw10QAhgA+/rUDO//saD//tAD49XHnFN/Ce3tnnqN2YMjwSN15l+waAVERYdLZ+yP0jf/mJze6kxYViWFQwrDY7is66/uRY3HVi8+UXk/bGGyc4O6+q6OvEZncmJEYgLkyLVqsN+8834HR1M378ZhGWbypGpakdw6OD8caim/CnB2/GiLhQhOuC8Pid6fjil7Pxr1OSIATw7v5LuO1/duKlHaVos3rnTCBrpx2PbCpGldmCQ5dMeHXnmas+56R0R5f7LrzrNWNkLDQqJcob23CmhreE99dABh+gx3k+AX6SNoMPBYyx+nC8+uNp+OTns3BHegJsdoFtR4xYvqkYU/9zO3721wP48NtyNPW4V6jTZsff9ji2uRZljejX17lpRDSCVAqUN7bhYv3QOs/H3YnNl1MoFLh1rPO2dtdtIOfBhdP6ERAdXyfS5esOhv6e2Hw5pbK7rf23H5fgrhe+xjen66BRK/HoHWPw+YrZbmt5hkeH4KUFN+L9h2bixpQotFptWLf9FOas24l3913EmZrmQS2C/u9tx3HokglateOf9/VfnkbpVbZ3Tw3gHV3uhGjUyBwZA4DbXZ4YyK0uAJg52hF8jpSbYGoL3PvTWNxMAWfisEi8+eDNOFpuwseHK/DZESPK6luRX2JEfonRcRv5mHj8yyQDOu0CFaZ2xIZq8N0pif16/RCNGlOGR2H/hQYUnq1FSmzvxdC+pNrcjuomC5QKuC1s7unWMfHYvPeiy0GG1k47DncFGF9Z8WmxdKK063yaKVc5sdmd28bF4/3iS1INTPb4BKz+bgZS+lErNC01Gn9/aCY+PlyJ3392AuWNbXj8/cMAAKUCGBYdjBGxoRgRG4rU2BCkxYUiNTYUKTEh0Kiv7WfSbUcqpe7DV340FZuKyrDjRDVW/v0Iti7L6vV4gcEOPoBj63BXaS12HK/GT74zctC+jr8QQkgrPgkDtOKTGBmMkXGhOFvbgqKzdZjby7Ec/o7BhwLWxGGRmDgsEivvTEdJhRmfHa3EZ0eMOFvbgs+PV+Hz41XS2AXTU1xOMb6aGSNjHcHnTB3uu3loBB9nfc+o+LBeby93mjkqDkqF45qDisY2JEUFo6TCBGunHTGhmn4XETsLnE9VOQqQr/Ubfm+OlptgF0BipA4JETqPn3/buHikxYVCAeDX88bjjvGedSQpFAr825QkzJ2gx5++OYdPDlXifF0LWq02XKx3rAhefgq2Rq3EY9lj8NDsUR7VJJ2vbcHj7zmC1c9mj8Id4/UYnxiBPX/4CgcuNOBve8uw0E13Wl2zBbXNjvNixgzSVhcA5GYY8Mynx7HnXB3KG9swLCp40L6WP2iydMLStTI4UCs+gGPV52xtCwrOMPgQBSyFQiGFoP87dxxOVTVj25FKfHa0EqeqmhGiUeFHMzwLL1mjYvHyl6exp6vOZyi0Njs7uvqq73GKDAnClOQoHCxrNddyWQAAIABJREFUxK7SGtx3c0p3fU9K/4uIh0cHI0KnhrndsTLjrj3+evTnRva+hOuC8MUvZ1/3/3+6IBUevm00Hr5ttOMn+WYLzte24nxdCy7UtUj/+3xtC1qsNjyXfxIX69vwn3dnQH2Vk6YBxwWjD/+tGM2WTkwfEYP/O3csACApKhi/yh2Hpz8+ht9/dgI54/UwRLoGQOdqVkpMyFUD7/VIjgnBjJEx2HO2Hn8/cAk/v2PMoH0tf1DbtdoTplUPaOv5zFFxeHtPWUCf58PgQ9SDQuG4mHOcIRy/yBmLszXNCFIpkRjp2U+n01KjoVEpYTS343xdK9LiQgdpxgOnPx1dPd06Jh4HyxrxdWmtI/hc6D6xub8UCgUykiJReLYOJeXmQQg+jvc0+Sqt9X0Z6NCqUCiQEK5DQrgO09NiXD4nhMBfCi/g6Y9LsHlvGYymNrx8/1SEXuWept99cgzHKs2IDdXgxQU3uoSlhVkj8OGhChwsa8RvPjyK1xdOc3lP3tjmcvrBtGTsOVuP94ov4ZE5o4fEDwRyGejCZidn88WpqmbUNFkG/PWHAhY3E/VhZHwYkmM8P4RQF6TCDSmOVYahcp6P89yZfgefrgLnb07XwmYX/T648HKDeZChs6Prhmtc8fE2hUKBB2aOwIYfT4MuSIkvT9bgvtcLUd3Ue0v8BwfLsamoDAoF8P/uu+GKFR2VUoFn75mMIJUC249VIf+o0eXzJ6ucd3QN3jaX012TDAjVqHChrhX7zg/Mier+qkYqbL72W9ndiQ7VYEJXDV+grvow+BANkqwhdJ5PbbMFlaZ2KBTdN6dfzZThUQjXqtHY2oHtx4yoNLVDpVRc9eDCyzmD1kAXONc1W3CpwdFV5+7eMV82N8OAzT+dgdhQDY6Wm/G99QU4XX1lZ9bp6iY8+b9HAAA/nzNGOlzycuMM4fjZ7FEAgNUflbh09AzWHV3uhGjUmDfZ0SSwdf/FQf96Q1ntIK34AMAto4f+nYLXg8GHaJA4DzLcc7bO58/zcW5zpcWFIuwq2ypOapVSao99/vNSAMD4xHCP60ScKz7HKs39Pmm4P5w3so+KD0XEVQ5T9EU3pkTj7w/PRFpcKMob23DPKwUuF++2Wjvx8N+K0Wq1YeaoWDx6lZqZ5bePxsj4UNQ0WfDsZ8cBOLbWuld8Bj/4AMAPbkoGAHx6pHJQT+we6gbynq7LOQ8y/IYrPkQ0kG5MiYJGrURNk8XnD20r6cfBhe44VxhOdK0aeLrNBTi2E3VBSrRabThfN3B/Tp7eyO6LUmND8f5DMzE1JQrm9k4sfHMvPjrkOEV89YclOFXVjPhwLV744Y1Q9dKq7qQLUuHZeyYDADbvvYg9Z+tgNLejqb0TaqUCI+MGf6sLAG5KjcaI2BC0Wm347LJtN+pWO0A3s7tzc1oM1F13Cl7tEl5/xOBDNEh0QSrpID9f3+46cq3BZ4zr1srVbmR3R6VUIN0w8Of5HL7Oji5fEROqwaafzsCdGQZYbXb8n80Hseyv+/HegUtQKoAXf3hjv7dDpqfFSJfsPvn3I9KqWFpc6IAfJdAbhUKBf582HADw3gFud/VGWvEZhK2uMK1a+oEgEOt8GHyIBlFW1xHxe3x8L93Zyu5pV1VyTIjLmT3XsuIDdJ8UPVAFzkIIqaNrKK/4OOmCVFj/o6n4j1vSAAD/KHGcMfXLueOk/8b6a+Vd6UgI1+JsbQt++1EJgMG7o6s390wdDoUC2HO2HmV1gbfi0B+D1dXlJF1f4eP/Ng0GBh+iQTQU6nzqW6wob3QUAWf0cVVFb5zbXXFhWgyPvrZD6ZyBq6R8YFZ8LjW0ob7FiiCVAuMTvftNfbColAqs/tcJ+M13J0CtVGDuBD0e6ipY9kRkcBB+d3cGAKDC5OgWG5vg3T+jpKhgzBrtqDN5r/iSx8///FgV/rT7HOwDWBPmawb6uorL9byw1Ff/bRosPMeHaBBNSY6ELkiJuhYrSqubvdI54ynnPVkjYkOuqQj436Yk4e09F3DnxCvvreqvni3tVzvwscXSiUOXGmHptKPTJmCz29FhE+js+tVmFzjWtWU2PjHCoxO3h4Ils9Jw703DEaZVX/Of950TEzF3gh7/POZYOfJGK/vl/n3acOwqrcX7By7hsTvG9HqdxuVKKkz42dsH0GkXaOuwYfntowdtjkII7CqtxceHKjBvciJuG5cwaF+rJ7tdSMFnsFZ8bkyJgrarBvF09f9v787jmryyPoD/shDCGjZJ2AU3BAQVBXEZrUTQasetM7hUbZ2p1WKrdavtW7cug9VR6za11Wm109el6qutVlSKilVZBFxABEEZUUkAUQgga3LfPwLRKEWChIA5388nHzXPJc/1GMPhPvc5pxzd2uFnk75Q4kOIHpnyeejnYYdzOfcRf7O4fSY+OtbveVq/znZI+DgUNmYtrzfSXWwFPpeDh49qISutgnMj7QxqlSrsTcrDV79lo7iiplmv2/sluMzVGKtWuEvt07F+iL9ZjEe1yhb/27+IcF8JrIR83CupRMKtYgysXwFqSk2dCgt/uoK6+pWedSez0NvNBoOa8bW6qFWq8OtVGb45ewvXZeok+lzOfZz/cHizE7QXUVpZi1ql+u9o38p1fBoITXjo31n92XQ+575RJT4tutS1detWdO7cGUKhEMHBwUhKSmpy/P79++Ht7Q2hUIhevXrh2LFjWscZY1i+fDmcnJxgZmYGqVSK7OxsrTGpqakYMWIEbGxsYG9vj1mzZqG8vFxrTF5eHkaPHg1zc3M4Ojpi8eLFqKuj2yWJYTXswWirmhkqFcPVuyWaD+znSW/hxuYnOVoJX2hzrNCEh66OllrzacAYQ0xGAUZ+dRbLfr6G4ooaiK1N4etsjQA3G/TzsMUALzsM7uqAYT06QdpTjJG+Evwl0BVvUzPMPyQRCXF47iDsnTUArra6F+l8UUITHl4LcAYAHEhp3uWuLaeykSkvg52FAKP9naBiwPt7LkFe+scFHnVRUV2Hf5/LxbC1ZzB/32VclylgZsKDgM+FrLRK0wJF3xpWe0RmJnpdsWz4bDpvZPt8dF7x2bdvHxYsWIBt27YhODgYX331FcLDw5GVlQVHx2eXAS9cuIDJkycjKioKY8aMwe7duzFu3DikpqbCz88PALBmzRps2rQJu3btgqenJ5YtW4bw8HBkZGRAKBQiPz8fUqkUERER2LJlCxQKBebPn48333wTBw4cAAAolUqMHj0aEokEFy5cgEwmw/Tp02FiYoJ//OMfLxgmQlpugJe6LUFCbjFUKqaXnxhLK2txLvs+TmUWIu5GIe6X14DLATZO6qP55vJHdG1VoS++ziJkystwLV+haZ6YdrcUXxzLQMKtBwDUdzh9IO2GSUHuMGlGDyvStC6dLNGl8ZqHbeL1QFfsTszDsXQZVo31bXIlK+1uKbaeuQkA+GysH0J7OiK3qAIZMgUid6di76wBLX5PFJZVYdeF/+I/8behqFL/sOxgKcCMkM54Y4AHlv9yDUeu5ON4ulynliwtpe+NzQ0GdXXA2hNZmh8sRviIIe0pRi8XUZusbBkKh+m4qyk4OBj9+/fHli1bAAAqlQpubm547733sHTp0mfGR0REoKKiAkePHtU8N2DAAPTu3Rvbtm0DYwzOzs5YuHAhFi1aBAAoLS2FWCzGzp07MWnSJHz77bdYtmwZZDIZuFz1GzstLQ3+/v7Izs5G165dER0djTFjxiA/Px9isbqD8rZt2/Dhhx+iqKgIAsHzlwsVCgVEIhFKS0thba37Jk9CGlOrVCFg1Ul13ZJ5Q9DT6cXfW4wxZBeW43RmIU5lFiL59kOt4n8CHhc1ShV4XA62TumLkX6Nd2EufVSLgE9PAgCuLA+DyNxwhf6+O5eLT49mQNpTjFVjffHPE1k4dOkeAHXH8r8N9sScYV06ZDFC0jjGGKTr43CzqAJfTuyFiP6NNwOurlPiz5vPI6ugDKP9nbB1Sl8AwO3iCozZfA5lVXWYOcgTy1/z0en8hWVV2BBzAwdT7qFGqe6E7ulggbeHeGFCXxcITdSrLdFpMsz531S42Znh7OJX9N5j7OfL9zBv72WEeNljz6wBejuPUsXw3p5UHE+X48l94o5WpgjtKcYIH0cM7OKgiUN7psv3b53S45qaGqSkpEAqlT5+AS4XUqkU8fHxjX5NfHy81ngACA8P14zPzc2FXC7XGiMSiRAcHKwZU11dDYFAoEl6AMDMTL0H4Ny5c5rz9OrVS5P0NJxHoVDg2rVrjc6turoaCoVC60FIazPhcdGvs3rVZ1Nstla7AF2VPKrBF79mYPCXpxG24SyiojORmPsAShVDl04WeHuIJ3a/HYyrK8MwoY+L5oPtVGZBo6/XsL/Hzc7MoEkP8HjF6XzOfbzyzzOapGd8HxecXjQMH470pqTnJcPhcDSVnPcn//Hlrk2x2cgqKIODpQCfjfXTPO9hb4F1fwkAAHx3Phe/XpU1+9znsu/j1Y2/Y0/SHdQoVejrboNtbwTitwVDMSXYXeub/dAenSA04eLOg8pWb63SmIYVHwc9r/jwuBz8a2ogUj4ZgfV/DcCr9b3UCsuqsScpDzN3JqPPpzGY9UMyfsto/DOkI9Ip8bl//z6USqVWcgEAYrEYcnnjFTjlcnmT4xt+bWrM8OHDIZfLsXbtWtTU1ODhw4ea1SWZTNbkeZ48x9OioqIgEok0Dzc3t6YDQEgLTQlyA4cDRKfLEb7hLE5nFur09YwxHEi5i9B1cdj+ey7ulVRCwOdiaPdOWPVnX5xd/ApiFw7D/4z20fyEtuZ1f4zxd0KtkmH2j6n4PbvomddtaeFCfWi47byyVomaOhUGeNnhyNzB2BDRGy6NbHYmL4fxfVzA5QDJtx/iVlH5M8ev3CnB1/WXuD4f5wc7C+3V+zBfCd4Zqt7LteTAFdxs5DWeVKdUYd3JLEz7LhH3y2vgLbHC/tkh+L93B2Gkn6TRCtjmAj5eqb+jKzq9+clVS+mzXUVjbC0EmNDXFf+aGojU5SOwa2YQpg3wgJNIiMpaJU5mFODvPyTj7I1nP0M6og5xkdzX1xe7du3CunXrYG5uDolEAk9PT4jFYq1VIF199NFHKC0t1Tzu3KEqokQ/Rvo5Yd+sEHS2N4dcUYW3dl7Ewp+uoPTR81d/sgvKMOnbBCzafwXFFTXoLrbEtjcCcbn+A2rGwM5wt392cyqfx8WGiN4I8xGjpk6Ft39IRsJTFaTT28n+HkB9p9Jf+7nC31WEHdP7Yc/bA9CrgzUXJboTWwsxtL4W1MGnavpU1SqxcP8VqJi6bMJIP6dGX2NxWA8Ee9qhokaJOT+m4FFN4ze1FCiqMGVHIjafygFjwOQgdxyOHIT+9SuyTRnVS33uY2lyvde9ebzio587uppiyudhaPdO+GycHy4sHY6j7w3GqPpL5aujM1+K2kk6ZQ0ODg7g8XgoKNBe8iooKIBE0vgeAolE0uT4hl+f95pTpkyBXC7HvXv3UFxcjJUrV6KoqAheXl5NnufJczzN1NQU1tbWWg9C9CXI0w7R8/6Evw/2BIej/pAfsSHuD5eQK2uUWHM8E6M2/o7E3AcQmnDx4UhvHH1vCEb6SZrVDNSEx8XmKX3wSo9OqKpVYebOi0i5/UBzXJP46FixWV/WvB6AX+YOhtSn5TWBSMfzeqB6tf1gyj2tvWobfruBnMJyOFiaYtWfff/w6/n17/NOVqa4UVCO/zmU/kxyEnejCKM2/o6k3AewEPCwcVJvRE3o1ez9K8O9HSHgc5F7v0LT2FVf7peryzW01YrPH+FwOPBzEeGL8b1gZcpHhkyBI1fzDTqn1qBT4iMQCBAYGIjY2FjNcyqVCrGxsQgJCWn0a0JCQrTGA0BMTIxmvKenJyQSidYYhUKBxMTERl9TLBbD0tIS+/btg1AoxIgRIzTnSUtLQ2Hh40sIMTExsLa2ho+PbhveCNEXMwEPn4zxwYHZIfBysEBhWTX+/kMyPth3GSWPHtemOZVZgBEb4vCvMzdRp2KQ9nREzAdDMWdYF51vGzfl8/D1G4EY3NUBj2qUePO7i7hypwSKqlr8t75dQHtY8SHGS+rjCJGZCeSKKpzPUfeOSs17iO1nbwEA/jHeD7YWTa9+OFoJsWWyulnroUv38GNiHgD1pa0vj2dixndJeFBRg55O1jjy3mCM7e2i0xwtTfma3nTRafptrtpWd3U1l52FALOHqauErz2Rheo6pYFn9GJ0vk60YMECbN++Hbt27cL169cxZ84cVFRU4K233gIATJ8+HR999JFm/Lx583D8+HGsW7cOmZmZWLlyJZKTkzF37lwA6oxy/vz5+Pzzz/HLL78gLS0N06dPh7OzM8aNG6d5nS1btiA1NRU3btzA1q1bMXfuXERFRcHGRl2gLCwsDD4+Ppg2bRquXLmCEydO4JNPPkFkZCRMTdvHm4eQBoEedjg2bwje+ZMXuBzg0KV7kK4/iwMpd/HOf5Ixc2cy7j6shLNIiG+nBWLHjP5ws2t5rRWhCQ/bp/dDkKcdyqrrMP27JM1mUhcbs2f2TRDSlkz5PIztrS67sD/lLqpqlVhUf4lrfB8XTXmD5wn2sseS8B4AgM+OZODENTkmb0/Q7BF6Y4A7Dr07EF6dWlap+tVe6nnoe5+P5lKXgVd8nvTWoM5wtDLF3YeV2F2fVHZUOtfxiYiIQFFREZYvXw65XI7evXvj+PHjmo3EeXl5WvtuBg4ciN27d+OTTz7Bxx9/jG7duuHw4cOaGj4AsGTJElRUVGDWrFkoKSnB4MGDcfz4cQiFQs2YpKQkrFixAuXl5fD29sY333yDadOmaY7zeDwcPXoUc+bMQUhICCwsLDBjxgx8+umnLQoMIfomNOHho1d7YqSfBIsPXEVOYTkW7b8CAOBzOfjbYE+8H9oNFqatU2DdTMDDd2/2x/R/JyI1rwSfHc0A8LhBKCGG9JdAN/wQfxsnrslhIeDhVlEFHK1MsULHW9Rn/ckLKbcf4mRGAd75TwoA9WrN6om9MMa/6ZpWzxPaUwwTHgc3CsqRU1iuKbrZmpQqhgcV6sTHsZ2s+ADqDd7zpd3x8aE0bD6Vg9cDXVulgrgh6FzH52VGdXyIoVTVKrExNhvbz95CH3cbfDbOD94S/bwHFVW1eGNHIq7Wdy9fFNYdc4d308u5CGkuxhhGbfwdmfLH+2f+PaMfQnuKm/iqxpVW1uLPW87hdvEj+LlYY+uUvvCwt2iVeb75fRLOZBXp7f9NUVk1+n/xGzgcIPvzUeC3o0KddUoVwjacxa37FXh/eFcsCOth6Clp6K2ODyFEP4QmPHw40hvpq8Kxf/ZAvSU9AGAtNMEPM4PgU19IsbX7HBHSEhwOB68Humr+PLGva4uSHkDd6mH/7BBsntwHB+cMbLWkBwBe9Xt8d5c+NFzmsjMXtKukB1BvIl8yUp3sbP89F4WK1mkV0tbaV1QJMXJtVSHVxlyAw5GDELd4WJuU4CekOcb1cYHIzARudmY6V2F+mqOVEK8FOLd6r6sRPmLwuBxkyBS4XVyh09ceT5fhnyeykFP4x7WG9N2V/UWF+0rQx90GlfWr1B0RJT6EGCkBn9uqPwkT8qIcLE1xZtEwHHt/CERm7XP/iK2FACFe6uae0enNX/VJv1eKyN2XsOV0DqTr4zB1RwKOp8tQV98qo0F7u6PraRwOB0tHegMA9l6802jRyfaOEh9CCCHthq2FoN1vmh3VcHdXWvPu7qpVqrDkwFUoVQwuNmbgcoDzOcWY/WMqBn95Gptis1FYpr5s1LDi057u6HpasJc9Qr0doVQx/PNklqGnozNKfAghhBAdhPlIwOEAV+6W4u7DR88d/+3ZW8iQKWBjboLDkYNwdskriHylC+wtBJArqrA+5gYGRp3C3N2pSMpVFxhtrys+DZaM9AaHo97rdCnvoaGnoxNKfAghhBAddLIyRVB9m4vjz7nclV1Qho2/qffCrHjNB52sTOFqa47F4d648NFwbJzUG4EetqhTMRy9KkNsfR8/Q1dtfp4eEitM7KvejL46OrPZbTwYY1rVuQ2BEh9CCCFER6/W9+5qap+PUsWw5OBV1ChVGO7tiHFPVYtWF250wcE5A/Hr+4MxOcgNZvU3OHQVt36NoNb2wYjuEPC5SMx9gDPPaWCaX1KJradzELo+DgdSDNsXkxIfQgghREfh9dWkU24/hLy08du6vz+fi0t5JbAy5eOL8X5N9p/zdRYhaoI/Ej4Oxa/vD9Z0g2/PXGzM8ObAzgCAL6Mzn1nJeVRTh4MpdzF1RwIGfXkKa09k4VZRBY5c0X+H+6a0TklYQgghxIhIREIEetgi5fZDnLgmx4z6BKDB7eIKzcbfj0f3hJPIrFmvKzIzgcis4/TOe3dYF+xNykOmvAyHL93D+D4uSMgtxsGUe4hOl+FRzeO+XgO87DChr6tmtcxQKPEhhBBCWmCUnwQptx/iWJpMK/FRqRg+PHgVVbUqDOxij0n93Qw3ST2zMRfg3Ve6YnV0JqKir2N9zA3cK6nUHPewN8fEvq4Y38flhfoNtiZKfAghhJAWGOknwee/XsfF/z5AUVm15k6sPRfzkHDrAcxMeFg9wb/JS1wvgzcHdsbO8/+FvL6Ss5UpH2MCnDCxrysCPWzb3d+fEh9CCCGkBVxtzRHgKsKVu6U4mSHH1GAP5JdUIupYJgBgcXgPuNu3j1UOfRKa8LBpch/8lHwHf+reCWE+4jarQt8SlPgQQgghLTSqlxOu3C1FdJocU4Lc8fGhNJRX1yHQw/aZfT8vsyBPOwR52hl6Gs1Cd3URQgghLTTKT313V/ytYvz7XC7OZBVBwOfiy4n+4HHb1yUeokaJDyGEENJCHvYW8HGyhlLF8Pmv1wEA86Xd0NWx/dfhMVaU+BBCCCEv4NX63l0A4OdijVlDvAw4G/I8lPgQQgghL2BUfV0aPpeDNRMDwOfRt9b2jDY3E0IIIS+gSydLfDstEJamfPg4Wxt6OuQ5KPEhhBBCXlCYr+T5g0i7QOtxhBBCCDEalPgQQgghxGhQ4kMIIYQQo0GJDyGEEEKMBiU+hBBCCDEalPgQQgghxGhQ4kMIIYQQo0GJDyGEEEKMBiU+hBBCCDEalPgQQgghxGhQ4kMIIYQQo0GJDyGEEEKMBiU+hBBCCDEa1J39CYwxAIBCoTDwTAghhBDSXA3ftxu+jzeFEp8nlJWVAQDc3NwMPBNCCCGE6KqsrAwikajJMRzWnPTISKhUKuTn58PKygocDqdVX1uhUMDNzQ137tyBtbV1q742eRbFu21RvNsWxbttUbzbVkvizRhDWVkZnJ2dweU2vYuHVnyewOVy4erqqtdzWFtb03+cNkTxblsU77ZF8W5bFO+2pWu8n7fS04A2NxNCCCHEaFDiQwghhBCjwVu5cuVKQ0/CWPB4PAwbNgx8Pl1hbAsU77ZF8W5bFO+2RfFuW/qMN21uJoQQQojRoEtdhBBCCDEalPgQQgghxGhQ4kMIIYQQo0GJDyGEEEKMBiU+bWDr1q3o3LkzhEIhgoODkZSUZOgpvRTOnj2L1157Dc7OzuBwODh8+LDWccYYli9fDicnJ5iZmUEqlSI7O9tAs+34oqKi0L9/f1hZWcHR0RHjxo1DVlaW1piqqipERkbC3t4elpaWmDhxIgoKCgw0447t66+/hr+/v6aIW0hICKKjozXHKdb6tXr1anA4HMyfP1/zHMW89axcuRIcDkfr4e3trTmuz1hT4qNn+/btw4IFC7BixQqkpqYiICAA4eHhKCwsNPTUOryKigoEBARg69atjR5fs2YNNm3ahG3btiExMREWFhYIDw9HVVVVG8/05RAXF4fIyEgkJCQgJiYGtbW1CAsLQ0VFhWbMBx98gCNHjmD//v2Ii4tDfn4+JkyYYMBZd1yurq5YvXo1UlJSkJycjOHDh2Ps2LG4du0aAIq1Pl28eBHffPMN/P39tZ6nmLcuX19fyGQyzePcuXOaY3qNNSN6FRQUxCIjIzV/ViqVzNnZmUVFRRlwVi8fAOzQoUOaP6tUKiaRSNjatWs1z5WUlDBTU1O2Z88eQ0zxpVNYWMgAsLi4OMaYOr4mJiZs//79mjHXr19nAFh8fLyhpvlSsbW1ZTt27KBY61FZWRnr1q0bi4mJYUOHDmXz5s1jjNH7u7WtWLGCBQQENHpM37GmFR89qqmpQUpKCqRSqeY5LpcLqVSK+Ph4A87s5Zebmwu5XK4Ve5FIhODgYIp9KyktLQUA2NnZAQBSUlJQW1urFXNvb2+4u7tTzF+QUqnE3r17UVFRgZCQEIq1HkVGRmL06NFasQXo/a0P2dnZcHZ2hpeXF6ZOnYq8vDwA+o81laDUo/v370OpVEIsFms9LxaLkZmZaaBZGQe5XA4Ajca+4RhpOZVKhfnz52PQoEHw8/MDoI65QCCAjY2N1liKeculpaUhJCQEVVVVsLS0xKFDh+Dj44PLly9TrPVg7969SE1NxcWLF585Ru/v1hUcHIydO3eiR48ekMlkWLVqFYYMGYL09HS9x5oSH0KIziIjI5Genq51TZ60vh49euDy5csoLS3FgQMHMGPGDMTFxRl6Wi+lO3fuYN68eYiJiYFQKDT0dF56o0aN0vze398fwcHB8PDwwE8//QQzMzO9npsudemRg4MDeDzeMzvRCwoKIJFIDDQr49AQX4p965s7dy6OHj2K06dPw9XVVfO8RCJBTU0NSkpKtMZTzFtOIBCga9euCAwMRFRUFAICArBx40aKtR6kpKSgsLAQffv2BZ/PB5/PR1xcHDZt2gQ+nw+xWEwx1yMbGxt0794dOTk5en9/U+KjRwKBAIGBgYiNjdU8p1KpEBsbi5CQEAPO7OXn6ekJiUSiFXuFQoHExESKfQsxxjB37lwcOnQIp06dgqenp9bxwMBAmJiYaMU8KysLeXl5FPNWolKpUF1dTbHWg9B5mXWjAAACDUlEQVTQUKSlpeHy5cuaR79+/TB16lTN7ynm+lNeXo6bN2/CyclJ/+/vF94eTZq0d+9eZmpqynbu3MkyMjLYrFmzmI2NDZPL5YaeWodXVlbGLl26xC5dusQAsPXr17NLly6x27dvM8YYW716NbOxsWE///wzu3r1Khs7dizz9PRklZWVBp55xzRnzhwmEonYmTNnmEwm0zwePXqkGTN79mzm7u7OTp06xZKTk1lISAgLCQkx4Kw7rqVLl7K4uDiWm5vLrl69ypYuXco4HA47efIkY4xi3RaevKuLMYp5a1q4cCE7c+YMy83NZefPn2dSqZQ5ODiwwsJCxph+Y02JTxvYvHkzc3d3ZwKBgAUFBbGEhARDT+mlcPr0aQbgmceMGTMYY+pb2pctW8bEYjEzNTVloaGhLCsry7CT7sAaizUA9v3332vGVFZWsnfffZfZ2toyc3NzNn78eCaTyQw36Q5s5syZzMPDgwkEAtapUycWGhqqSXoYo1i3hacTH4p564mIiGBOTk5MIBAwFxcXFhERwXJycjTH9RlrDmOMvfi6ESGEEEJI+0d7fAghhBBiNCjxIYQQQojRoMSHEEIIIUaDEh9CCCGEGA1KfAghhBBiNCjxIYQQQojRoMSHEEIIIUaDEh9CCCGEGA1KfAghhBBiNCjxIYQQQojRoMSHEEIIIUaDEh9CCCGEGI3/B21JG/ji1WeLAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5bCzFaWHQvPM"
      },
      "source": [
        "# Making Final Test DataFrame"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zP-ySKBHQQ0x"
      },
      "source": [
        "last_section = df_train_scaled.iloc[-520:,:]\r\n",
        "\r\n",
        "final_test_df = last_section.append(df_test_scaled)"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QVZtE-DsRrHx"
      },
      "source": [
        "X_test_scaled, y_test_scaled = batcher(520,final_test_df , output_column)"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MEFENUl1SmaR"
      },
      "source": [
        "# Predictions."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hvR8fyNpRshP"
      },
      "source": [
        "yp_test_scaled = model.predict(X_test_scaled)"
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5ZIEQIZUSRcm",
        "outputId": "98909cbc-4da3-4dc2-87fe-ed574f210a8e"
      },
      "source": [
        "len(yp_test_scaled) , len(y_test_scaled)"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3935, 3935)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I37G3u9USiyL"
      },
      "source": [
        "#Back-Scaling to original."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E5M_KJTdST7M"
      },
      "source": [
        "c = scaler.scale_[0]"
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sXn_6aQgUJwT",
        "outputId": "e885a91f-0fc9-4a10-c903-31511f89546f"
      },
      "source": [
        "scaler.scale_"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.00093458, 0.01428571, 0.04048583, 0.02806886, 0.01538462])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tqj4yl2qSpqK"
      },
      "source": [
        "yp , y_test = yp_test_scaled/c , y_test_scaled/c"
      ],
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0tpXQ67xS5fe"
      },
      "source": [
        "t_test = final_test_df.index[520:]"
      ],
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "uECcbxlPS9xj",
        "outputId": "283250a0-ea1c-4131-f516-c56a505bc1fb"
      },
      "source": [
        "plt.plot(t_test,y_test)\r\n",
        "plt.plot(t_test , yp)"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fc27407d780>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "foQQsIHqTBrB"
      },
      "source": [
        "yp_train = model.predict(X_train_scaled)/c\r\n",
        "y_train = y_train_scaled/c"
      ],
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g8jWsEleTdn_"
      },
      "source": [
        "t_train = df_train.index[:-520]"
      ],
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 385
        },
        "id": "BDSbhKSrTiae",
        "outputId": "a18aab99-a40d-406b-fb90-e2f4fb0f1019"
      },
      "source": [
        "plt.figure(figsize=(12,4))\r\n",
        "plt.plot(t_train,y_train)\r\n",
        "plt.plot(t_train,yp_train)"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fc27415f358>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1200x400 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mm0AR0KNTk2h"
      },
      "source": [
        "# model.predict(X_train)"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4tQ7OIpY0Itc"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}